Our proposed calibrated-\(\delta \)  adjustment MI method for missing data in a binary/categorical covariate involves utilising population-level information about the incomplete covariate to generate a calibrated-\(\delta \)  adjustment, which is then used in the intercept of the imputation model in order to improve the analysis of data suspected to be MNAR. The development of this method was motivated by van Buuren et al.'s [1]} \(\delta \)  (offset) approach in MI, but where \(\delta \)  is derived based on external information instead of chosen arbitrarily or based on expert's belief (which is arguably not arbitrary, but can be subjective). Direct linkage to external data has also increasingly been used for the analysis of missing data generated by a MNAR mechanism.[2]} However, external linked data might not always be available, or the linkage might not be possible, whereas our proposed calibrated-\(\delta \)  adjustment MI method does not require records from the same individuals to be directly linked between the datasets.
